867 resultados para Economics of information theory


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This paper examines how US and proposed international law relate to the recovery of archaeological data from historic shipwrecks. It argues that US federal admiralty law of salvage gives far less protection to historic submerged sites than do US laws protecting archaeological sites on US federal and Indian lands. The paper offers a simple model in which the net present value of the salvage and archaeological investigation of an historic shipwreck is maximized. It is suggested that salvage law gives insufficient protection to archaeological data, but that UNESCO's Convention on the Protection of the Underwater Cultural Heritage goes too far in the other direction. It is also suggested that a move towards maximizing the net present value of a wreck would be promoted if the US admiralty courts explicitly tied the size of salvage awards to the quality of the archaeology performed.

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The eminent domain clause of the U.S. Constitution concerns the limits of the government's right to take private property for public use. The economic literature on this issue has examined (1) the proper scope of this power as embodied by the 'public use' requirement, (2) the appropriate definition, and implications, of 'just compensation,' and (3) the impact of eminent domain on land use incentives of owners whose land is subject to a taking risk. This essay reviews this literature and draws implications for our understanding of eminent domain law.

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The salvage of historic shipwrecks involves a debate between salvors, who wish to maximize profit, and archeologists, who wish to preserve historical value. Traditionally, salvage of shipwrecks has been governed by admiralty law, but the Abandoned Shipwreck Act of 1987 transferred title of historically important wrecks in U.S. waters to the state in whose waters the wreck is found, thereby abrogating admiralty law. This paper examines incentives to locate and salvage historic wrecks under traditional admiralty law and proposes an efficient reward scheme. It then re-considers current U.S. and international law in light of the results.

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Objective. The purpose of the study is to provide a holistic depiction of behavioral & environmental factors contributing to risky sexual behaviors among predominantly high school educated, low-income African Americans residing in urban areas of Houston, TX utilizing the Theory of Gender and Power, Situational/Environmental Variables Theory, and Sexual Script Theory. ^ Methods. A cross-sectional study was conducted via questionnaires among 215 Houston area residents, 149 were women and 66 were male. Measures used to assess behaviors of the population included a history of homelessness, use of crack/cocaine among several other illicit drugs, the type of sexual partner, age of participant, age of most recent sex partner, whether or not participants sought health care in the last 12 months, knowledge of partner's other sexual activities, symptoms of depression, and places where partner's were met. In an effort to determine risk of sexual encounters, a risk index employing the variables used to assess condom use was created categorizing sexual encounters as unsafe or safe. ^ Results. Variables meeting the significance level of p<.15 for the bivariate analysis of each theory were entered into a binary logistic regression analysis. The block for each theory was significant, suggesting that the grouping assignments of each variable by theory were significantly associated with unsafe sexual behaviors. Within the regression analysis, variables such as sex for drugs/money, low income, and crack use demonstrated an effect size of ≥±1, indicating that these variables had a significant effect on unsafe sexual behavioral practices. ^ Conclusions. Variables assessing behavior and environment demonstrated a significant effect when categorized by relation to designated theories. ^

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Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude